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    CORR
    2010
    Springer
    110views Education» more  CORR 2010»
    Improving Term Extraction Using Particle Swarm Optimization Techniques
    13 years 5 months ago
    Improving Term Extraction Using Particle Swarm Optimization Techniques
    Download  www.scipub.org
    : Problem statement: Term extraction is one of the layers in the ontology development process which has the task to extract all the terms contained in the input document automatica...
    Mohammad Syafrullah, Naomie Salim
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    CSSE
    2008
    IEEE
    170views Software Engineering» more  CSSE 2008»
    The Design and Implementation of Ontology and Rules Based Knowledge Base for Transportation
    13 years 7 months ago
    The Design and Implementation of Ontology and Rules Based Knowledge Base for Transportation
    Download  www.isprs.org
    : The traditional transportation information inquiry mainly uses key words based on the text, and the service the inquiry system provided is onefold and only aims at one transporti...
    Gang Cheng, Qingyun Du, Hongli Ma
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    BMCBI
    2005
    251views more  BMCBI 2005»
    Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation
    13 years 5 months ago
    Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation
    Download  www.biomedcentral.com
    Background: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very effici...
    Tapio Pahikkala, Filip Ginter, Jorma Boberg, Jouni...
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    EMNLP
    2008
    174views Natural Language Processing» more  EMNLP 2008»
    Relative Rank Statistics for Dialog Analysis
    13 years 7 months ago
    Relative Rank Statistics for Dialog Analysis
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    We introduce the relative rank differential statistic which is a non-parametric approach to document and dialog analysis based on word frequency rank-statistics. We also present a...
    Juan Huerta
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    ICCV
    2003
    IEEE
    131views Computer Vision» more  ICCV 2003»
    Feature Selection for Unsupervised and Supervised Inference: the Emergence of Sparsity in a Weighted-based Approach
    14 years 7 months ago
    Feature Selection for Unsupervised and Supervised Inference: the Emergence of Sparsity in a Weighted-based Approach
    Download  www.cs.huji.ac.il
    The problem of selecting a subset of relevant features in a potentially overwhelming quantity of data is classic and found in many branches of science. Examples in computer vision...
    Lior Wolf, Amnon Shashua
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